code: def provide_sklearn_output_if_possible(df, x, target):
      # Reference generated by sklearn on the same data
      try:
          imp.find_module("sklearn_gbmi")
          sklearn_gbmi_found = True
      except ImportError:
          sklearn_gbmi_found = False
  
      # Hardcode numbers when CI do not have the needed libs
      x1x2_sklearn = 0.08209119711536361
      x1x3_sklearn = 0.14613801828239015
      x2x3_sklearn = 0.6938075033110082
      if sklearn_gbmi_found:
          print("Obtaining actual sklearn output")
          from sklearn_gbmi import h_all_pairs
          from sklearn.ensemble import GradientBoostingClassifier
  
          X = df[x]
          y = df[[target]]
          gbm_sklearn = GradientBoostingClassifier(
              n_estimators=100,
              random_state=1234,
              max_depth=2,
              learning_rate=0.1,
              min_samples_leaf=1,
          )
          gbm_sklearn.fit(X, np.ravel(y))
  
          sklearn_h = h_all_pairs(gbm_sklearn, X)
  
          x1x2_sklearn = sklearn_h.get(("X1", "X2"))
          x1x3_sklearn = sklearn_h.get(("X1", "X3"))
          x2x3_sklearn = sklearn_h.get(("X2", "X3"))
  
      return x1x2_sklearn, x1x3_sklearn, x2x3_sklearn
  
file_path: h2o-3/h2o-py/tests/testdir_hstats/pyunit_h_pubdev-8768_synthetic_data.py
imports: from builtins import range
  import sys, os
  import h2o
  from tests import pyunit_utils, assert_equals
  from h2o.estimators import H2OGradientBoostingEstimator, H2OXGBoostEstimator
  import numpy
  import pandas
  import imp
label_desc: Use image node_id 2 for calling a global function with example usage: provide_sklearn_output_if_possible(df, x, target) and returns: x1x2_sklearn,  x1x3_sklearn,  x2x3_sklearn